






'''simple docstring'''
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase="attention"				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
   lowercase_						:     Dict							     =  params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
   lowercase_						:     int							     =  params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
   lowercase_						:     Tuple							     =  params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
   return k, o, q, v




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=False				):


   """simple docstring"""




   if split_mlp_wi:
      lowercase_						:     Optional[int]							     =  params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
      lowercase_						:     Optional[int]							     =  params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
      lowercase_						:     int							     =  (wi_a, wi_a)
   else:
      lowercase_						:     Union[str, Any]							     =  params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

   lowercase_						:     Tuple							     =  params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
   return wi, wo




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		*, _UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  traverse_util.flatten_dict(variables["target"]				)
   lowercase_						:     Optional[Any]							     =  {"/".join(_UpperCamelCase				): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   lowercase_						:     str							     =  "encoder/layers_0/mlp/wi_0/kernel" in old
   print("Split MLP:"    ,		_UpperCamelCase				)

   lowercase_						:     int							     =  collections.OrderedDict()

   # Shared embeddings.
   lowercase_						:     str							     =  old["token_embedder/embedding"]

   # Encoder.
   for i in range(_UpperCamelCase				):
      # Block i, layer 0 (Self Attention).
      lowercase_						:     int							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"pre_attention_layer_norm"				)
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     int							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"attention"				)
      lowercase_						:     int							     =  layer_norm
      lowercase_						:     str							     =  k.T
      lowercase_						:     List[Any]							     =  o.T
      lowercase_						:     Optional[Any]							     =  q.T
      lowercase_						:     Union[str, Any]							     =  v.T

      # Block i, layer 1 (MLP).
      lowercase_						:     Any							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"pre_mlp_layer_norm"				)
      lowercase_     ,		lowercase_						:     Any							     =  tax_mlp_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		_UpperCamelCase				)
      lowercase_						:     str							     =  layer_norm
      if split_mlp_wi:
         lowercase_						:     Tuple							     =  wi[0].T
         lowercase_						:     List[str]							     =  wi[1].T
      else:
         lowercase_						:     List[Any]							     =  wi.T
      lowercase_						:     int							     =  wo.T

   lowercase_						:     Any							     =  old[
       "encoder/relpos_bias/rel_embedding"
   ].T
   lowercase_						:     Optional[Any]							     =  old["encoder/encoder_norm/scale"]

   if not is_encoder_only:
      # Decoder.
      for i in range(_UpperCamelCase				):
         # Block i, layer 0 (Self Attention).
         lowercase_						:     List[str]							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_self_attention_layer_norm"				)
         lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     str							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"self_attention"				)
         lowercase_						:     List[Any]							     =  layer_norm
         lowercase_						:     int							     =  k.T
         lowercase_						:     str							     =  o.T
         lowercase_						:     Union[str, Any]							     =  q.T
         lowercase_						:     int							     =  v.T

         # Block i, layer 1 (Cross Attention).
         lowercase_						:     int							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_cross_attention_layer_norm"				)
         lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Any							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"encoder_decoder_attention"				)
         lowercase_						:     str							     =  layer_norm
         lowercase_						:     str							     =  k.T
         lowercase_						:     Tuple							     =  o.T
         lowercase_						:     List[str]							     =  q.T
         lowercase_						:     Union[str, Any]							     =  v.T

         # Block i, layer 2 (MLP).
         lowercase_						:     Optional[Any]							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_mlp_layer_norm"				)
         lowercase_     ,		lowercase_						:     Tuple							     =  tax_mlp_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		_UpperCamelCase				)
         lowercase_						:     Tuple							     =  layer_norm
         if split_mlp_wi:
            lowercase_						:     str							     =  wi[0].T
            lowercase_						:     int							     =  wi[1].T
         else:
            lowercase_						:     List[Any]							     =  wi.T
         lowercase_						:     Dict							     =  wo.T

      lowercase_						:     int							     =  old["decoder/decoder_norm/scale"]
      lowercase_						:     Optional[int]							     =  old[
          "decoder/relpos_bias/rel_embedding"
      ].T

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         lowercase_						:     Any							     =  old["decoder/logits_dense/kernel"].T

   return new




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     int							     =  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      lowercase_						:     List[Any]							     =  state_dict["shared.weight"]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         lowercase_						:     List[str]							     =  state_dict["shared.weight"]

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("Using shared word embeddings as lm_head."				)
         lowercase_						:     Union[str, Any]							     =  state_dict["shared.weight"]

   return state_dict




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  checkpoints.load_tax_checkpoint(_UpperCamelCase				)
   lowercase_						:     Any							     =  convert_tax_to_pytorch(_UpperCamelCase    ,		num_layers=config.num_layers    ,		is_encoder_only=_UpperCamelCase				)
   lowercase_						:     int							     =  make_state_dict(_UpperCamelCase    ,		_UpperCamelCase				)
   model.load_state_dict(_UpperCamelCase    ,		strict=_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = False				):


   """simple docstring"""




   lowercase_						:     int							     =  TaConfig.from_json_file(_UpperCamelCase				)
   print(F"""Building PyTorch model from configuration: {config}"""				)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      lowercase_						:     int							     =  TaEncoderModel(_UpperCamelCase				)
   else:
      lowercase_						:     Union[str, Any]							     =  TaForConditionalGeneration(_UpperCamelCase				)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
   model.save_pretrained(_UpperCamelCase				)

   # Verify that we can load the checkpoint.
   model.from_pretrained(_UpperCamelCase				)
   print("Done"				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
 )
 UpperCamelCase__			=      parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
 )





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@staticmethod
		@abstractmethod
		def   _snake_case       (       snake_case__     :     ArgumentParser		)      ->	Tuple:
				raise NotImplementedError()



		@abstractmethod
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[Any]:
				raise NotImplementedError()